Package Dependencies/ Data

Load Packages

First I need to load up the packages I’ll need

library(sf)
## Linking to GEOS 3.4.2, GDAL 2.1.2, proj.4 4.9.1
library(ggplot2) #development version!
## devtools::install_github("tidyverse/ggplot2")
library(tidyverse)
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag():    dplyr, stats
library(readr)
library(cowplot)
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggplot2':
## 
##     ggsave
library(sp)
library(gridExtra)
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
library(dplyr)
library(ggrepel)
library(plyr)
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
## 
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following object is masked from 'package:purrr':
## 
##     compact

Import Postcode Data

Now I import my data. I filter for the Arran postcodes, (since Arran all begins ‘KA27’).

#Add download commands for data.
## Finding the Arran coordinates
arrancoordinates <- read.csv("../alldata/ukpostcodes.csv") %>%
 filter(substr(postcode,1,4)=="KA27")

#Find way to replace with existing SIMD shape files
arransubsect <- read_sf("../alldata/Scotland_pcs_2011") %>%
filter(substr(label,1,4)=="KA27")

Import SIMD data

Now I load the SIMD data, containing the geometries (shapefiles) and SIMD data (percentiles, etc)

reorderedvector<- c("S01011174", "S01011171", "S01011177", "S01011176", "S01011175", "S01011173", "S01011172" )

arran2016 <- read_sf("../alldata/SG_SIMD_2016")[c(4672,4666,4669,4671,4667,4668,4670),] %>%
  slice(match(reorderedvector, DataZone))

Arrandz2012 <- c(4409,4372,4353,4352,4351,4350,4349)

arran2012 <- read_sf("../alldata/SG_SIMD_2012")[Arrandz2012,]
arran2009 <- read_sf("../alldata/SG_SIMD_2009")[Arrandz2012,]
arran2006 <- read_sf("../alldata/SG_SIMD_2006")[Arrandz2012,]
arran2004 <- read_sf("../alldata/SG_SIMD_2004")[Arrandz2012,]

sharedvariables <- intersect(colnames(arran2016), colnames(arran2012)) %>%
  intersect(colnames(arran2009))  %>%
  intersect(colnames(arran2006))  %>%
  intersect(colnames(arran2004))
  
arran20162 <- arran2016 %>%
  select(sharedvariables) %>%
  mutate(year="2016")
arran20122 <- arran2012 %>%
  select(sharedvariables) %>%
  mutate(year="2012")
arran20092 <- arran2009 %>%
  select(sharedvariables) %>%
  mutate(year="2009")
arran20062 <- arran2006 %>%
  select(sharedvariables) %>%
  mutate(year="2006")
arran20042 <- arran2004 %>%
  select(sharedvariables) %>%
  mutate(year="2004")

arransimd <- rbind(arran20162,arran20122,arran20092,arran20062,arran20042) %>%
mutate(
    lon = map_dbl(geometry, ~st_centroid(.x)[[1]]),
    lat = map_dbl(geometry, ~st_centroid(.x)[[2]])
    )

arransimd$listID <- revalue(arransimd$DataZone,
               c("S01004409"="S01004409/S01011174", "S01004372"="S01004372/S01011171", "S01004353"="S01004353/S01011177", "S01004352"="S01004352/S01011176", "S01004351"="S01004351/S01011175", "S01004350"="S01004350/S01011173", "S01004349"="S01004349/S01011172", "S01011174"="S01004409/S01011174", "S01011171"="S01004372/S01011171", "S01011177"="S01004353/S01011177", "S01011176"="S01004352/S01011176", "S01011175"="S01004351/S01011175", "S01011173"="S01004350/S01011173", "S01011172"="S01004349/S01011172"))

Now I want to overlay the postcodes by Datazone. To do this I’ve converted both the Arran coordinates and Arran (2016) shapefiles into spatial points/polygons, converted them into a common CRS, and then compared them by using ‘plyr::over()’. This gives me the object ‘namingdzpostcode’, with the postcode rows grouped into IDs (unidentified datazones).

simple.sf <- st_as_sf(arrancoordinates, coords=c('longitude','latitude'))
st_crs(simple.sf) <- 4326

exampleshapes <- sf:::as_Spatial(arran2016$geometry) %>%
  spTransform(CRS("+proj=longlat +datum=WGS84"))

examplepoints <- sf:::as_Spatial(simple.sf$geom) %>%
  spTransform(CRS("+proj=longlat +datum=WGS84"))

namingdzpostcode <- over(exampleshapes, examplepoints, returnList = TRUE)

I can then take a member reference from the orginal postcode list, which gives me a selection of the rows in that DZ. For simplicity I’ve written this as a new function. ##Mutate arrancoordinates to label the IDs

function100 <- function(argument) 
{
  argument <- arrancoordinates[namingdzpostcode[[argument]],] %>% mutate(DataZone=argument)
}

arrancoordinates <- lapply(1:7,function100)
arrancoordinates <- rbind(arrancoordinates[[1]], arrancoordinates[[2]], arrancoordinates[[3]], arrancoordinates[[4]], arrancoordinates[[5]], arrancoordinates[[6]], arrancoordinates[[7]])

arrancoordinates$listID <- revalue(as.character(arrancoordinates$DataZone),
               c('1'="S01004409/S01011174", '2'="S01004372/S01011171", '3'="S01004353/S01011177", '4'="S01004352/S01011176", '5'="S01004351/S01011175", '6'="S01004350/S01011173", '7'="S01004349/S01011172"))

Labelling the namingdzpostcode list

names(namingdzpostcode) <- c(unique(arransimd$listID))

//

Mapping

library(rgdal)
## rgdal: version: 1.2-7, (SVN revision 660)
##  Geospatial Data Abstraction Library extensions to R successfully loaded
##  Loaded GDAL runtime: GDAL 2.1.2, released 2016/10/24
##  Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/gdal
##  Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
##  Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/proj
##  Linking to sp version: 1.2-4
library(leaflet)
library(ggmap)
## 
## Attaching package: 'ggmap'
## The following object is masked from 'package:cowplot':
## 
##     theme_nothing

Coordinates

postcodelist <- paste(unique(arrancoordinates$listID), "Postcodes", sep=" ")
datazonelist <- paste(unique(arrancoordinates$listID), "Datazones", sep=" ")

m = leaflet() %>% addTiles() %>% setView(-5.227680, 55.582338, zoom = 10)

Map1

m %>% 

#allcoordinates
addMarkers(
    lng = arrancoordinates$longitude, lat = arrancoordinates$latitude,
    label = arrancoordinates$postcode,
    labelOptions = labelOptions(noHide = F), group = "Postcode Plots") %>%
hideGroup("All Postcode Plots") %>% 

#alldatazones  
addPolygons(data=exampleshapes, 
            weight = 2, 
            label = datazonelist,
            group = "All Datazones") %>% 
hideGroup("Datazones") %>% 
  
#selectcoordinates
addMarkers(
    lng = arrancoordinates$longitude, lat = arrancoordinates$latitude,
    label = arrancoordinates$postcode,
    labelOptions = labelOptions(noHide = F), group = arrancoordinates$listID) %>% 
hideGroup(arrancoordinates$listID) %>% 

#selectdatazone
addPolygons(data = exampleshapes[1] , 
            weight = 2, label = datazonelist[1], group = datazonelist[1]) %>% 
addPolygons(data = exampleshapes[2] , 
            weight = 2, label = datazonelist[2], group = datazonelist[2]) %>% 
addPolygons(data = exampleshapes[3] , 
            weight = 2, label = datazonelist[3], group = datazonelist[3]) %>% 
addPolygons(data = exampleshapes[4] , 
            weight = 2, label = datazonelist[4], group = datazonelist[4]) %>% 
addPolygons(data = exampleshapes[5] , 
            weight = 2, label = datazonelist[5], group = datazonelist[5]) %>% 
addPolygons(data = exampleshapes[6] , 
            weight = 2, label = datazonelist[6], group = datazonelist[6]) %>% 
addPolygons(data = exampleshapes[7] , 
            weight = 2, label = datazonelist[7], group = datazonelist[7]) %>% 
hideGroup(datazonelist[1]) %>%
hideGroup(datazonelist[2]) %>%
hideGroup(datazonelist[3]) %>%
hideGroup(datazonelist[4]) %>%
hideGroup(datazonelist[5]) %>%
hideGroup(datazonelist[6]) %>%
hideGroup(datazonelist[7]) %>%

#Layers control
addLayersControl(
    baseGroups = c("All Datazones", "Postcode Plots", "Nothing"),
    overlayGroups = c(arrancoordinates$listID, datazonelist),
    options = layersControlOptions(collapsed = TRUE)
  )

Example Markers

Inputing example markers.

cliniccoordinates <- read.csv("../alldata/clinics.csv") %>%
dplyr::left_join(arrancoordinates, by="postcode")
## Warning: Column `postcode` joining factors with different levels, coercing
## to character vector
#change to character
cliniccoordinates$X <- as.character(cliniccoordinates$X)

Map2

m %>%

#allcoordinates
addMarkers(
    lng = arrancoordinates$longitude, lat = arrancoordinates$latitude,
    label = arrancoordinates$postcode,
    labelOptions = labelOptions(noHide = F), group = "All Postcode Plots") %>%
hideGroup("All Postcode Plots") %>% 

#alldatazones  
addPolygons(data=exampleshapes, 
            weight = 2, 
            label = datazonelist,
            group = "All Datazones",
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("All Datazones") %>% 

#cliniccoordinates
addMarkers(
    lng = cliniccoordinates$longitude, lat = cliniccoordinates$latitude,
    label = cliniccoordinates$X,
    labelOptions = labelOptions(noHide = F), group = "All GP clinics") %>%
  hideGroup("All GP clinics") %>%   

#cliniccoordinates
addMarkers(
    lng = cliniccoordinates$longitude, lat = cliniccoordinates$latitude,
    label = cliniccoordinates$X,
    labelOptions = labelOptions(noHide = F), group = cliniccoordinates$X) %>%
  hideGroup(cliniccoordinates$X) %>%   
  
#Layers control
addLayersControl(
    baseGroups = c("All Datazones", "All Postcode Plots", "All GP clinics", "Nothing"),
    overlayGroups = c(cliniccoordinates$X),
    options = layersControlOptions(collapsed = TRUE)
  )

Map3

Overlaying percentiles

exampleshapes2 <- as(arransimd, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

Creating a palate for percentiles

pal2 <- colorNumeric(
  palette = "viridis",
  domain = exampleshapes2$Percentile)

Creating the percentile labels

listlistlist <- paste(datazonelist, exampleshapes2$Percentile, sep=" ") %>%
paste("%", sep="")

Map

m %>% 

#alldatazones  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2004, ], 
            weight = 2, 
            label = listlistlist[29:35],
            group = "2004",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2004") %>% 
  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2006, ], 
            weight = 2, 
            label = listlistlist[22:28],
            group = "2006",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2006") %>% 
  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2009, ], 
            weight = 2, 
            label = listlistlist[15:21],
            group = "2009",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2009") %>% 
  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2012, ], 
            weight = 2, 
            label = listlistlist[8:14],
            group = "2012",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2012") %>% 

addPolygons(data=exampleshapes2[exampleshapes2$year == 2016, ], 
            weight = 2, 
            label = listlistlist[1:7],
            group = "2016",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2016") %>% 
  
#cliniccoordinates
addMarkers(
    lng = cliniccoordinates$longitude, lat = cliniccoordinates$latitude,
    label = cliniccoordinates$X,
    labelOptions = labelOptions(noHide = F), group = cliniccoordinates$X) %>%
  hideGroup(cliniccoordinates$X) %>%   

addLegend("bottomleft", pal = pal2, values = exampleshapes2$Percentile,
    title = "SIMD Percentile",
    labFormat = labelFormat(suffix = "%"),
    opacity = 1
  )  %>%  

#Layers control
addLayersControl(
    baseGroups = c("2004", "2006", "2009", "2012", "2016", "Nothing"),
    overlayGroups = c(cliniccoordinates$X),
    options = layersControlOptions(collapsed = TRUE)
  )

Map4 Arran vs. Scotland

Beautiful map, but I have to leave the laptop running overnight to compile it.

#Import UK data
DZBoundaries2016 <- read_sf("../alldata/SG_SIMD_2016")
Scotland2016 <- as(DZBoundaries2016, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

DZBoundaries2012 <- read_sf("../alldata/SG_SIMD_2012")
Scotland2012 <- as(DZBoundaries2012, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

DZBoundaries2009 <- read_sf("../alldata/SG_SIMD_2009")
Scotland2009 <- as(DZBoundaries2009, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

DZBoundaries2006 <- read_sf("../alldata/SG_SIMD_2006")
Scotland2006 <- as(DZBoundaries2006, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

DZBoundaries2004 <- read_sf("../alldata/SG_SIMD_2004")
Scotland2004 <- as(DZBoundaries2004, "Spatial") %>%
spTransform(CRS("+proj=longlat +datum=WGS84"))

#Colour Palate
pal2 <- colorNumeric(
  palette = "viridis",
  domain = 0:100)

leaflet() %>% 
  addTiles() %>% 
  setView(-5.227680, 55.582338, zoom = 10) %>% 

addPolygons(data=exampleshapes2[exampleshapes2$year == 2004, ], 
            weight = 2, 
            label = listlistlist[29:35],
            group = "2004",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2004") %>% 
  
addPolygons(data=Scotland2004, 
            weight = 2, 
            group = "Scotland 2004",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("Scotland 2004") %>% 
  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2006, ], 
            weight = 2, 
            label = listlistlist[22:28],
            group = "2006",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2006") %>% 

addPolygons(data=Scotland2006, 
            weight = 2, 
            group = "Scotland 2006",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("Scotland 2006") %>% 
  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2009, ], 
            weight = 2, 
            label = listlistlist[15:21],
            group = "2009",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2009") %>% 

addPolygons(data=Scotland2009, 
            weight = 2, 
            group = "Scotland 2009",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("Scotland 2009") %>% 
  
addPolygons(data=exampleshapes2[exampleshapes2$year == 2012, ], 
            weight = 2, 
            label = listlistlist[8:14],
            group = "2012",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2012") %>% 
  
addPolygons(data=Scotland2012, 
            weight = 2, 
            group = "Scotland 2012",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("Scotland 2012") %>% 

addPolygons(data=exampleshapes2[exampleshapes2$year == 2016, ], 
            weight = 2, 
            label = listlistlist[1:7],
            group = "2016",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("2016") %>% 

addPolygons(data=Scotland2016, 
            weight = 2, 
            group = "Scotland 2016",
            fillOpacity =0.8,
            color = ~pal2(Percentile),
            highlightOptions = highlightOptions(color = "black", weight = 2,
      bringToFront = TRUE)) %>% 
hideGroup("Scotland 2016") %>% 
  
addMarkers(
    lng = cliniccoordinates$longitude, lat = cliniccoordinates$latitude,
    label = cliniccoordinates$X,
    labelOptions = labelOptions(noHide = F), group = cliniccoordinates$X) %>%
  hideGroup(cliniccoordinates$X) %>%   

addLegend("bottomleft", pal = pal2, values = exampleshapes2$Percentile,
    title = "SIMD Percentile",
    labFormat = labelFormat(suffix = "%"),
    opacity = 1
  )  %>%  

addLayersControl(
    baseGroups = c("2004", "Scotland 2004", "2006", "Scotland 2006", "2009", "Scotland 2009", "2012", "Scotland 2012", "2016", "Scotland 2016", "Nothing"),
    overlayGroups = c(cliniccoordinates$X),
    options = layersControlOptions(collapsed = TRUE)
  )

Map4 Arran vs. Scotland Map

Map4

Map4

Map5

I’ll continue this on a new document so I can knit it to an html website.

Map5.

Map5 Code.

But the development is that I’ve used intersect() on each filtered ‘SG_SIMD_…’ to create the ‘sharedvariables’ vector, so I was able to create a new dataframe of every year with only shared variables, rather than percentile alone which I originally used as an example to practice the maps on (my previous version of ‘arransimd’). The new ‘arranSIMD’ can then have a version of map3 for each shared variable.

Go back.